1. Technical Field
The present invention relates, in general, to a method and apparatus which yield an automated analysis of waveform representations of heart function. In particular, the present invention relates to a method and apparatus which yield an automated analysis of waveform representations of heart function produced by an electrocardiographic device.
2. Description of Related Art
The electrocardiogram (EKG) is a graphic recording of the electrical potentials generated by electrical activity in the heart. The electrical impulse formation and conduction associated with each cardiac contraction produce weak electrical currents that spread through the entire body. By applying electrodes to various positions on the body, and connecting these electrodes to an electrocardiographic apparatus, the variation in the magnitude of the electrical potential is recorded.
A normal EKG consists of a series of waves which are repeated with each cardiac cycle. These waves are labeled as P, QRS, and T according to convention. The P wave represents the depolarization and contraction of both atria, the QRS complex represents the depolarization and contraction of the ventricles, and the T wave represents the repolarization of the ventricles.
An arrhythmia (irregular heart beat) exists when the normal cardiac conduction is disturbed or interrupted. Arrhythmias can occur in many different forms, and have historically been grouped according to different characteristics of the arrhythmia. Arrhythmias can be grouped based on rate: bradyarrhythmias (rate too slow) and tachyarrhythmias (rate too fast). Arrhythmias can be grouped based on the originating site in the heart: atrial arrhythmias, junctional arrhythmias, and ventricular arrhythmias. And, arrhythmias can be grouped based on the underlying pathophysiologic mechanism of the arrhythmia: conduction abnormalities (caused by conduction block, reentry, or reflection), and impulse formation abnormalities (caused by altered automatic or triggered activity). While some arrhythmias are totally asymptomatic and benign (they do not affect the circulation, nor do they warn of the development of more serious arrhythmias), others can be symptomatic and life threatening (due to their impairment of the heart's ability to pump enough blood to meet the body's demands), eventually causing significant mortality and morbidity.
Ventricular fibrillation (VF) is a lethal arrhythmia. Its most frequent cause is coronary artery disease, and it is the most common terminal event in sudden cardiac death. VF occurs when multiple ectopic ventricular foci produce complete disruption of the normal order of ventricular excitation, resulting in a quivering motion of the ventricles. The surface EkG pattern is characterized by a rapid, repetitive series of chaotic waves without any identifiable QRS complexes. Due to the lack of coordinated electrical and mechanical activity, the heart becomes an ineffective pump and circulatory arrest occurs within seconds. The patient will die within minutes unless a normal spontaneous rhythm is restored, usually by electric defibrillation. Therefore, rapid and accurate recognition of VF is very important so that appropriate therapies can be initiated promptly.
Ventricular tachycardia (VT) is another arrhythmia which originated in the ventricles with a rate greater than 100 beats per minute by definition. There are several different forms of VT, including monomorphic (uniform QRS morphology), polymorphic (constantly changing QRS morphology), torsades de points (polymorphic VT with QT interval prolongation), and ventricular flutter (sinusoidal morphology). While nonsustained VT (VT with short duration and without hemodynamic collapse) is not immediately life threatening, VT with very rapid rate and/or long duration can cause serious hemodynamic deterioration and is always potentially life threatening. Clinical data has shown that most sudden cardiac death patients had VT as their initiating event that degenerated into ventricular fibrillation. Therefore, it is highly desirable to detect these immediate forerunners of VF episodes so that appropriate therapies can be initiated.
For this reason, several forms of VT, including high rate monomorphic VT, polymorphic VT (including torsades de points), and ventricular flutter are usually considered in the same life threatening category as ventricular fibrillation when designing automated detection methods for VF detection. Therefore, even though these detection methods are usually called VF detection method, they are in fact designed to detect both ventricular fibrillation and several forms of potentially life-threatening ventricular tachycardia as described above. From the detection method design viewpoint this is actually desirable, because due to the peculiar QRS morphologies associated with these VT waveforms, which are often -intermediary between VT and VF, it is very difficult to differentiate them from the true VF episodes.
A great amount of work has been done over the past twenty years to develop computer programs for automated VF detection. At present, the automated VF detection capability is an essential component in three major cardiac care devices, including (1) Real-time EKG/arrhythmia monitors, (2) Implantable cardioverter-defibrillators (ICDs), and (3) Automatic external defibrillators (AEDs). Clinical values of these devices in terms of life saving and morbidity reduction are well established despite the fact that the VF detection methods used in these devices are not perfect. Accuracy of these automated detection methods are measured in terms of false negative (true VF episode not detected) and false positive (non-VF episode detected as VF). For a given detection method design tradeoffs between false positive and false negative can usually be made by adjusting the detection threshold. While missed detection of a life-threatening tachyrhythmia episode may have significant impact on a patient's morbidity and mortality, false positive detection on the other hand has the potential of causing the patient to receive inappropriate treatments, which may also have undesirable consequences. Therefore, the ultimate goal of performance improvement is to reduce both the false negative and false positive, which cannot be achieved by just changing the detection threshold.
There are many different techniques which are currently utilized for automated VF/VT detection. One such technique has been described by S. Kuo and R. Dillman in Computer Detection of Ventricular Fibrillation, Computers in Cardiology, 1978, pp. 347-349, IEEE Computer Society, which is incorporated by reference herein in its entirety. The functioning of which will be briefly explained with reference to FIG. 3.
With reference to FIG. 3, the method can be briefly described as follows. First, the incoming EKG signal 300 is fed to AND Converter 302 which produces a sampled EKG signal V.sub.(j), which is then fed into Period Computation Unit 304. Period Computation Unit 304 calculates a period estimate T utilizing the following equation: ##EQU1## Second, using the estimated period derived from Equation 1, the sampled EKG signal is fed into VF-Index Computation Unit 306 wherein it is shifted by half a period (i.e., T/2) and added to the original signal. The sum of the absolute values of the resultant residual signal is then computed and normalized within VF-Index Computation Unit 306 utilizing the following equation: ##EQU2## Note that here, for sake of clarity, the VF filter leakage, as Equation 2 is described in the foregoing cited reference, is described here as the VF-Index. Thereafter, the computed VF-Index is fed to Threshold Comparison Unit 308, wherein the VF-Index is compared to a threshold value. Currently, VF is declared if the VF-Index is small (i.e., is less than a preselected threshold). (See Kuo and Dillman page 348).
In rough overview, what the foregoing method does is analogize the waveform to a sinusoidal with estimated period T approximated by Equation 1, "slip" the analogized waveform a half period, and sum the original and "slipped" waveform. Thus, for a waveform that has some degree of regularity (i.e., has some degree of regularity from cycle to cycle within the wave), the summation will have a minimum at a delay of about one-half the amount of time that is required for the waveform to completely repeat itself.
The VF/VT automated detection method as described works well when the waveforms presented resemble the sinusoidal (e.g. ventricular flutter). The symmetric characteristics of the waveform produce excellent cancellation after copies of the signal have been shifted and superimposed onto the original signal. However, when the signals become more chaotic, the cancellation after superimposition is poor (producing a large normalized residual) hence making it more difficult to detect ventricular fibrillation.
Over the past eighteen year period, significant effort has been made to increase the overall accuracy of the described VF/VT automated detection method. Currently utilized methods are to use various and different VF filter leakage threshold levels in order to attempt to detect various and different arrhythmias. However, such currently utilized methods which try to detect the more chaotic arrhythmias (e.g., using a higher threshold for detection) tend to be problematical (e.g., allowing a detector to be more sensitive, under presently utilized methods, also generally increases the likelihood of false positive detection; the converse also tends to be true).
Thus, it is apparent from the foregoing that a need exists for a method and apparatus which yield an automated analysis of waveform representations of heart function produced by an electrocardiographic device, and where such method and apparatus enhance both the sensitivity and selectivity of the automated detection of arrhythmias within even highly chaotic waveform representations of heart function.